Three-step risk inference in insurance ratemaking

As catastrophic events happen more and more frequently, accurately forecasting risk at a high level is vital for the financial stability of the insurance industry. This paper proposes an efficient three-step procedure to deal with the semicontinuous property of insurance claim data and forecast extr...

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Bibliographic Details
Published in:Insurance, mathematics & economics mathematics & economics, 2022-07, Vol.105, p.1-13
Main Authors: Hou, Yanxi, Kang, Seul Ki, Lo, Chia Chun, Peng, Liang
Format: Article
Language:English
Subjects:
Automobile insurance
Bootstrap finance
Bootstrap method
Catastrophic events
Claims
Forecasting
Generalized Pareto distribution
Insurance
Insurance claims
Insurance industry
Insurance loss
Logistic regression
Motor cars
Pareto optimum
Quantile regression
Random weighted bootstrap
Regression analysis
Regression models
Risk
Statistical analysis
Statistical methods
Uncertainty
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Summary:As catastrophic events happen more and more frequently, accurately forecasting risk at a high level is vital for the financial stability of the insurance industry. This paper proposes an efficient three-step procedure to deal with the semicontinuous property of insurance claim data and forecast extreme risk. The first step uses a logistic regression model to estimate the nonzero claim probability. The second step employs a quantile regression model to select a dynamic threshold for fitting the loss distribution semiparametrically. The third step fits a generalized Pareto distribution to exceedances over the selected dynamic threshold. Combining these three steps leads to an efficient risk forecast. Furthermore, a random weighted bootstrap method is employed to quantify the uncertainty of the derived risk forecast. Finally, we apply the proposed method to an automobile insurance data set.
ISSN:0167-6687
1873-5959
DOI:10.1016/j.insmatheco.2022.03.005